refactor: remove multi-provider support, use Anthropic SDK directly

- Remove provider_utils.py (241 lines of adapter code)
- Simplify all agent files to use Anthropic SDK directly
- Update model to claude-sonnet-4-5-20250929
- Add python-dotenv with override=True (.env takes priority over env vars)
- Simplify .env.example to only require ANTHROPIC_API_KEY

This keeps the codebase focused on teaching agent concepts
rather than API compatibility layers. Users who need other
providers can use tools like litellm or one-api.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.env.example

@@ -1,21 +1,3 @@
-# Provider Selection (defaults to anthropic for backward compatibility)
+# Anthropic API Key (required)
-AI_PROVIDER=anthropic  # Options: anthropic, openai, gemini, or any OpenAI-compatible service
+# Get your key at: https://console.anthropic.com/
-
+ANTHROPIC_API_KEY=sk-ant-xxx
-# Model Name (auto-defaults based on provider, but can be overridden)
-MODEL_NAME=kimi-k2-turbo-preview
-
-# Anthropic Configuration
-ANTHROPIC_API_KEY=sk-xxx
-ANTHROPIC_BASE_URL=https://api.moonshot.cn/anthropic
-
-# OpenAI Configuration
-OPENAI_API_KEY=sk-xxx
-OPENAI_BASE_URL=https://api.openai.com/v1
-
-# Google Gemini Configuration (via OpenAI-compatible endpoint)
-GEMINI_API_KEY=xxx
-GEMINI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/
-
-# Example: Custom OpenAI-compatible service
-# CUSTOM_API_KEY=xxx
-# CUSTOM_BASE_URL=https://api.custom-service.com/v1

README.md

@@ -38,11 +38,11 @@ A progressive tutorial that demystifies AI coding agents like Kode, Claude Code,
 
 ```bash
 # Install dependencies
-pip install -r requirements.txt
+pip install anthropic python-dotenv
 
-# Configure your API
+# Configure API key
 cp .env.example .env
-# Edit .env with your API key (supports Anthropic, OpenAI, Gemini, etc.)
+# Edit .env with your ANTHROPIC_API_KEY
 
 # Run any version
 python v0_bash_agent.py  # Minimal

README_zh.md

@@ -37,11 +37,12 @@
 ## 快速开始
 
 ```bash
+# 安装依赖
 pip install anthropic python-dotenv
 
-# 配置 API
+# 配置 API Key
 cp .env.example .env
-# 编辑 .env 填入你的 API key
+# 编辑 .env 填入你的 ANTHROPIC_API_KEY
 
 # 运行任意版本
 python v0_bash_agent.py  # 极简版

provider_utils.py

@@ -1,242 +0,0 @@
-"""
-Provider utilities for multi-provider AI agent support.
-
-This module provides a unified interface for multiple AI providers (Anthropic, OpenAI, Gemini),
-allowing the existing agent code (v0-v4) to run unchanged.
-
-It uses the Adapter Pattern to make OpenAI-compatible clients look exactly like
-Anthropic clients to the consuming code.
-"""
-
-import os
-import json
-from typing import Any, Dict, List, Union, Optional
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-# =============================================================================
-# Data Structures (Mimic Anthropic SDK)
-# =============================================================================
-
-class ResponseWrapper:
-    """Wrapper to make OpenAI responses look like Anthropic responses."""
-    def __init__(self, content, stop_reason):
-        self.content = content
-        self.stop_reason = stop_reason
-
-class ContentBlock:
-    """Wrapper to make content blocks look like Anthropic content blocks."""
-    def __init__(self, block_type, **kwargs):
-        self.type = block_type
-        for key, value in kwargs.items():
-            setattr(self, key, value)
-            
-    def __repr__(self):
-        attrs = ", ".join(f"{k}={v!r}" for k, v in self.__dict__.items())
-        return f"ContentBlock({attrs})"
-
-# =============================================================================
-# Adapters
-# =============================================================================
-
-class OpenAIAdapter:
-    """
-    Adapts the OpenAI client to look like an Anthropic client.
-    
-    Key Magic:
-    self.messages = self 
-    
-    This allows the agent code to call:
-    client.messages.create(...)
-    
-    which resolves to:
-    adapter.create(...)
-    """
-    def __init__(self, openai_client):
-        self.client = openai_client
-        self.messages = self  # Duck typing: act as the 'messages' resource
-
-    def create(self, model: str, system: str, messages: List[Dict], tools: List[Dict], max_tokens: int = 8000):
-        """
-        The core translation layer. 
-        Converts Anthropic inputs -> OpenAI inputs -> OpenAI API -> Anthropic outputs.
-        """
-        # 1. Convert Messages (Anthropic -> OpenAI)
-        openai_messages = [{"role": "system", "content": system}]
-        
-        for msg in messages:
-            role = msg["role"]
-            content = msg["content"]
-            
-            if role == "user":
-                if isinstance(content, str):
-                    # Simple text message
-                    openai_messages.append({"role": "user", "content": content})
-                elif isinstance(content, list):
-                    # Tool results (User role in Anthropic, Tool role in OpenAI)
-                    for part in content:
-                        if part.get("type") == "tool_result":
-                            openai_messages.append({
-                                "role": "tool",
-                                "tool_call_id": part["tool_use_id"],
-                                "content": part["content"] or "(no output)"
-                            })
-                        # Note: Anthropic user messages can also contain text+image, 
-                        # but v0-v4 agents don't use that yet.
-
-            elif role == "assistant":
-                if isinstance(content, str):
-                    # Simple text message
-                    openai_messages.append({"role": "assistant", "content": content})
-                elif isinstance(content, list):
-                    # Tool calls (Assistant role)
-                    # Anthropic splits thought (text) and tool_use into blocks
-                    # OpenAI puts thought in 'content' and tools in 'tool_calls'
-                    text_parts = []
-                    tool_calls = []
-                    
-                    for part in content:
-                        # Handle both dicts and objects (ContentBlock)
-                        if isinstance(part, dict):
-                            part_type = part.get("type")
-                            part_text = part.get("text")
-                            part_id = part.get("id")
-                            part_name = part.get("name")
-                            part_input = part.get("input")
-                        else:
-                            part_type = getattr(part, "type", None)
-                            part_text = getattr(part, "text", None)
-                            part_id = getattr(part, "id", None)
-                            part_name = getattr(part, "name", None)
-                            part_input = getattr(part, "input", None)
-
-                        if part_type == "text":
-                            text_parts.append(part_text)
-                        elif part_type == "tool_use":
-                            tool_calls.append({
-                                "id": part_id,
-                                "type": "function",
-                                "function": {
-                                    "name": part_name,
-                                    "arguments": json.dumps(part_input)
-                                }
-                            })
-                    
-                    assistant_msg = {"role": "assistant"}
-                    if text_parts:
-                        assistant_msg["content"] = "\n".join(text_parts)
-                    if tool_calls:
-                        assistant_msg["tool_calls"] = tool_calls
-                    
-                    openai_messages.append(assistant_msg)
-
-        # 2. Convert Tools (Anthropic -> OpenAI)
-        openai_tools = []
-        for tool in tools:
-            openai_tools.append({
-                "type": "function",
-                "function": {
-                    "name": tool["name"],
-                    "description": tool["description"],
-                    "parameters": tool["input_schema"]
-                }
-            })
-
-        # 3. Call OpenAI API
-        # Note: Gemini/OpenAI handle max_tokens differently, but usually support the param
-        response = self.client.chat.completions.create(
-            model=model,
-            messages=openai_messages,
-            tools=openai_tools if openai_tools else None,
-            max_tokens=max_tokens
-        )
-
-        # 4. Convert Response (OpenAI -> Anthropic)
-        message = response.choices[0].message
-        content_blocks = []
-
-        # Extract text content
-        if message.content:
-            content_blocks.append(ContentBlock("text", text=message.content))
-
-        # Extract tool calls
-        if message.tool_calls:
-            for tool_call in message.tool_calls:
-                content_blocks.append(ContentBlock(
-                    "tool_use",
-                    id=tool_call.id,
-                    name=tool_call.function.name,
-                    input=json.loads(tool_call.function.arguments)
-                ))
-
-        # Map stop reasons: OpenAI "stop"/"tool_calls" -> Anthropic "end_turn"/"tool_use"
-        # OpenAI: stop, length, content_filter, tool_calls
-        finish_reason = response.choices[0].finish_reason
-        if finish_reason == "tool_calls":
-            stop_reason = "tool_use"
-        elif finish_reason == "stop":
-            stop_reason = "end_turn"
-        else:
-            stop_reason = finish_reason # Fallback
-
-        return ResponseWrapper(content_blocks, stop_reason)
-
-# =============================================================================
-# Factory Functions
-# =============================================================================
-
-def get_provider():
-    """Get the current AI provider from environment variable."""
-    return os.getenv("AI_PROVIDER", "anthropic").lower()
-
-def get_client():
-    """
-    Return a client that conforms to the Anthropic interface.
-    
-    If AI_PROVIDER is 'anthropic', returns the native Anthropic client.
-    Otherwise, returns an OpenAIAdapter wrapping an OpenAI-compatible client.
-    """
-    provider = get_provider()
-
-    if provider == "anthropic":
-        from anthropic import Anthropic
-        base_url = os.getenv("ANTHROPIC_BASE_URL")
-        # Return native client - guarantees 100% behavior compatibility
-        return Anthropic(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            base_url=base_url
-        )
-    
-    else:
-        # For OpenAI/Gemini, we wrap the client to mimic Anthropic
-        try:
-            from openai import OpenAI
-        except ImportError:
-            raise ImportError("Please install openai: pip install openai")
-
-        if provider == "openai":
-            api_key = os.getenv("OPENAI_API_KEY")
-            base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
-        elif provider == "gemini":
-            api_key = os.getenv("GEMINI_API_KEY")
-            # Gemini OpenAI-compatible endpoint
-            base_url = os.getenv("GEMINI_BASE_URL", "https://generativelanguage.googleapis.com/v1beta/openai/")
-        else:
-            # Generic OpenAI-compatible provider
-            api_key = os.getenv(f"{provider.upper()}_API_KEY")
-            base_url = os.getenv(f"{provider.upper()}_BASE_URL")
-
-        if not api_key:
-            raise ValueError(f"API Key for {provider} is missing. Please check your .env file.")
-
-        raw_client = OpenAI(api_key=api_key, base_url=base_url)
-        return OpenAIAdapter(raw_client)
-
-def get_model():
-    """Return model name from environment variable."""
-    model = os.getenv("MODEL_NAME")
-    if not model:
-        raise ValueError("MODEL_NAME environment variable is missing. Please set it in your .env file.")
-    return model

requirements.txt

@@ -1,5 +1,2 @@
 anthropic>=0.25.0
-openai>=1.0.0
 python-dotenv>=1.0.0
-pygame==2.5.2
-numpy==1.24.3

v0_bash_agent.py

@@ -47,14 +47,17 @@ Usage:
     python v0_bash_agent.py "explore src/ and summarize"
 """
 
-from provider_utils import get_client, get_model
+from anthropic import Anthropic
+from dotenv import load_dotenv
 import subprocess
 import sys
 import os
 
-# Initialize API client and model using provider utilities
+load_dotenv(override=True)
-client = get_client()
+
-MODEL = get_model()
+# Initialize Anthropic client (uses ANTHROPIC_API_KEY env var)
+client = Anthropic()
+MODEL = "claude-sonnet-4-5-20250929"
 
 # The ONE tool that does everything
 # Notice how the description teaches the model common patterns AND how to spawn subagents

v0_bash_agent_mini.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """v0_bash_agent_mini.py - Mini Claude Code (Compact)"""
-from provider_utils import get_client, get_model; import subprocess as sp, sys, os
+from anthropic import Anthropic; from dotenv import load_dotenv; import subprocess as sp, sys, os
-C = get_client(); M = get_model()
+load_dotenv(override=True); C = Anthropic(); M = "claude-sonnet-4-5-20250929"
 T = [{"name":"bash","description":"Shell cmd. Read:cat/grep/find/rg/ls. Write:echo>/sed. Subagent(for complex subtask): python v0_bash_agent_mini.py 'task'","input_schema":{"type":"object","properties":{"command":{"type":"string"}},"required":["command"]}}]
 S = f"CLI agent at {os.getcwd()}. Use bash to solve problems. Spawn subagent for complex subtasks: python v0_bash_agent_mini.py 'task'. Subagent isolates context and returns summary. Be concise."
 

v1_basic_agent.py

@@ -51,16 +51,10 @@ import subprocess
 import sys
 from pathlib import Path
 
+from anthropic import Anthropic
 from dotenv import load_dotenv
 
-# Load configuration from .env file
+load_dotenv(override=True)
-load_dotenv()
-
-# Import unified client provider
-try:
-    from provider_utils import get_client, get_model
-except ImportError:
-    sys.exit("Error: provider_utils.py not found. Please ensure you are in the project root.")
 
 
 # =============================================================================
@@ -68,8 +62,8 @@ except ImportError:
 # =============================================================================
 
 WORKDIR = Path.cwd()
-MODEL = get_model()
+MODEL = "claude-sonnet-4-5-20250929"
-client = get_client()
+client = Anthropic()
 
 
 # =============================================================================

v2_todo_agent.py

@@ -61,14 +61,10 @@ import subprocess
 import sys
 from pathlib import Path
 
+from anthropic import Anthropic
 from dotenv import load_dotenv
 
-load_dotenv()
+load_dotenv(override=True)
-
-try:
-    from provider_utils import get_client, get_model
-except ImportError:
-    sys.exit("Error: provider_utils.py not found. Please ensure you are in the project root.")
 
 
 # =============================================================================
@@ -77,8 +73,8 @@ except ImportError:
 
 WORKDIR = Path.cwd()
 
-client = get_client()
+client = Anthropic()
-MODEL = get_model()
+MODEL = "claude-sonnet-4-5-20250929"
 
 
 # =============================================================================

v3_subagent.py

@@ -79,14 +79,10 @@ import sys
 import time
 from pathlib import Path
 
+from anthropic import Anthropic
 from dotenv import load_dotenv
 
-load_dotenv()
+load_dotenv(override=True)
-
-try:
-    from provider_utils import get_client, get_model
-except ImportError:
-    sys.exit("Error: provider_utils.py not found. Please ensure you are in the project root.")
 
 
 # =============================================================================
@@ -95,8 +91,8 @@ except ImportError:
 
 WORKDIR = Path.cwd()
 
-client = get_client()
+client = Anthropic()
-MODEL = get_model()
+MODEL = "claude-sonnet-4-5-20250929"
 
 
 # =============================================================================

v4_skills_agent.py

@@ -84,14 +84,10 @@ import sys
 import time
 from pathlib import Path
 
+from anthropic import Anthropic
 from dotenv import load_dotenv
 
-load_dotenv()
+load_dotenv(override=True)
-
-try:
-    from provider_utils import get_client, get_model
-except ImportError:
-    sys.exit("Error: provider_utils.py not found. Please ensure you are in the project root.")
 
 
 # =============================================================================
@@ -101,8 +97,8 @@ except ImportError:
 WORKDIR = Path.cwd()
 SKILLS_DIR = WORKDIR / "skills"
 
-client = get_client()
+client = Anthropic()
-MODEL = get_model()
+MODEL = "claude-sonnet-4-5-20250929"
 
 
 # =============================================================================